1fa0f82f4c
The required signature of variable_creator is to only take **kwargs. *args makes it confusing if you want to modify the arguments passed to the next creator. PiperOrigin-RevId: 292739441 Change-Id: I0f56066a9cc927446a7e38a18f0d48aee290fb74
99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Code for creating a dataset out of a NumPy array."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.util import nest
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def init_var_from_numpy(input_var, numpy_input, session):
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"""Initialize `input_var` to `numpy_input` using `session` in graph mode."""
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with ops.init_scope():
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if context.executing_eagerly():
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input_var.assign(numpy_input)
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return
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assert session is not None
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session.run(input_var.initializer)
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start_placeholder = array_ops.placeholder(dtypes.int64, ())
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end_placeholder = array_ops.placeholder(dtypes.int64, ())
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slice_placeholder = array_ops.placeholder(input_var.dtype)
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assign_slice_op = input_var[start_placeholder:end_placeholder].assign(
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slice_placeholder)
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# If each batch element is > 64 MB, then we copy each batch element
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# individually. Otherwise, the slices will be < 128 MB. There might be
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# padding which might mean that the slices are 128 MB even if the size of
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# the tensor allocated is less than 128 MB. This formula gives slices with
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# size: ceil(64 MB / byte size per batch element) bytes. Using ceil()
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# guarantees we get a number >= 1.
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# Calculate the size of each batch element.
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byte_size_per_batch_element = (
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np.prod(numpy_input.shape[1:]) * input_var.dtype.size)
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# Calculate number of elements we want to copy per slice.
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batch_size_per_slice = int(
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np.ceil((64 << 20) / byte_size_per_batch_element))
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# Copy slices of the above size starting at 0, except the last slice will be
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# smaller.
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start = 0
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limit = numpy_input.shape[0]
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while start < limit:
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end = min(start + batch_size_per_slice, limit)
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session.run(assign_slice_op, feed_dict={
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start_placeholder: start,
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end_placeholder: end,
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slice_placeholder: numpy_input[start:end]})
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start = end
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def one_host_numpy_dataset(numpy_input, colocate_with, session):
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"""Create a dataset on `colocate_with` from `numpy_input`."""
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def create_colocated_variable(next_creator, **kwargs):
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kwargs["colocate_with"] = colocate_with
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return next_creator(**kwargs)
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numpy_flat = nest.flatten(numpy_input)
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with variable_scope.variable_creator_scope(create_colocated_variable):
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vars_flat = tuple(variable_scope.variable(array_ops.zeros(i.shape, i.dtype),
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trainable=False)
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for i in numpy_flat)
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for v, i in zip(vars_flat, numpy_flat):
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init_var_from_numpy(v, i, session)
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vars_nested = nest.pack_sequence_as(numpy_input, vars_flat)
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return dataset_ops.Dataset.from_tensor_slices(vars_nested)
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class SingleDevice(object):
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"""Used with `colocate_with` to create a non-mirrored variable."""
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def __init__(self, device):
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self.device = device
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